Getting started – Jira Data Center¶
Verified on a live Structure for Jira Data Center instance.
1. Prepare Jira and Structure¶
- Fields on the Epic: Team, Story Points (estimate) and PI (single select; the values are
the PI names, e.g.
PI 27.1). - Structure: create a structure with the generators Insert issues (JQL: the Epics of your area) and Add issues belonging to epics. Do not add filters – aggregates only see the rows that are displayed.
- Notes type for PI-Cap: create one in Structure and note its id (e.g.
dc_note_1).
2. Generate the views¶
Open the builder, choose Jira Data Center, enter the field ids, the Notes type and your PI calendar, and download the ZIP. Or on the command line:
python3 -m foreca3t --config foreca3t-config.json
3. Import¶
Import each JSON file as a view in Structure (dc-pi-view.json = full view, …-mgmt-release.json,
…-mgmt-art.json = management views). Importing again replaces the old version. Never edit the
formulas in Structure by hand: every building block is copied into many columns; change the
config and regenerate instead.
4. Quick transformations¶
Group the Epics with quick transformations – click them in this order for each view:
| View | Order |
|---|---|
| Versions (Release Outlook) | Version → ARTs → Team |
| Teams (ART / Train Status) | ARTs → Team → Version |
| PI | ARTs → Team → PI |
| Quick transformation | Type | Formula / field |
|---|---|---|
| Version | Group by → Attribute → Formula (variable fixversion = Fix Version/s) |
IF issuetype = "Epic" : fixversion.name |
| ARTs | Group by → Attribute → Formula (variable team = Team) |
see below: one line per ART |
| Team | Group by → Field | Team |
| PI | Group by → Field | PI |
// ART of the Epic's team: one regex line per ART, whole team name
CASE(team,
"/^(TEAM-A1|TEAM-A2)$/", "ART 1",
"/^(TEAM-B1|TEAM-B2)$/", "ART 2")
5. PI-Cap and check¶
Enter PI-Cap per team in the PI-Cap column on the team row. Then check one Epic per team by hand (Open, Util, Forecast). A team without PI-Cap shows ⓘ NO VELO.